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Construct Validity Issues in the Measurement of Motivation to Learn

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 Construct Validity Issues in the Measurement of Motivation to Learn

AnneMarie M. Conley and Stuart A. Karabenick

University of Michigan, Ann Arbor

Mailing address:

1400 SEB, 610 East University

Combined Program in Education and Psychology

The University of Michigan

Ann Arbor, MI 48109

Phone-734-763-1386

Fax-734-615-2164

Email: ampm@umich.edu

Presented at the biennial meeting of the Society for Research on Adolescence, San
Francisco, March 2006. Research reported herein was supported by a grant to the
Math and Science Partnership – Motivation Assessment Program (MSP-MAP) from
the National Science Foundation (EHR No. 0335369). Views expressed are the
authors’ and are not necessarily representative of the funding agency.
 Validity and Motivation to Learn 2

Construct Validity Issues in the Measurement of Motivation to Learn

Motivation plays a critical role in student learning and achievement; it is intimately

related to the ways students think, feel, and act in schools. Evidence from research on student

learning in general (see Pintrich & Schunk, 2002; Pintrich & Maehr, 2004), and mathematics and

science in particular (e.g., Fennema, 1989; Schoenfeld, 1992), demonstrates that students’

motivation, affect, strategies, and beliefs about knowledge in these disciplines can influence their

learning and performance. Furthermore, research suggests that students’ motivation and related

outcomes are sensitive to characteristics of the learning context, including teachers’ instructional

practices as well as school and classroom climate (Ames, 1992; Anderman & Maehr, 1999;

Eccles & Midgley, 1989). It is important, therefore, for reform efforts to determine how their

programs affect student motivation, especially since such changes can precede, or even occur in

the absence of, targeted cognitive outcomes. The primary goal of the research reported on here

was to develop and make available reliable, valid, and practical tools to assess student

motivational beliefs for mathematics and science. These tools are being used with different math

and science reform projects to support evidence-based claims about the effects of their

interventions, and to explore the role of motivation-related outcomes as mediators and

moderators of student achievement in intervention models (Maehr & Karabenick, 2004).

There exist a number of different approaches to the study of motivation. For example,

consider three different approaches to the question, “What makes students want to learn in

school?” One approach is to consider this question in terms of interest, as an individuals’

attraction to, or liking or enjoyment of, a particular task or domain. Another perspective

conceptualizes “wanting” in terms of value, a subjective judgment of the degree to which a task
 Validity and Motivation to Learn 3

or domain can fulfill needs, facilitate reaching goals, or confirm aspects of one’s self-schema. A

third approach attends to students’ goals, or their reasons for participating in achievement-related

activities. There often is considerable overlap among these constructs, with consistent, moderate

correlations among them. Though some have highlighted the importance of doing research that

considers these components simultaneously, such research has been limited to date. This paper

presents results from a large-scale study of middle and high school students that aimed to address

definition and measurement issues by considering multiple constructs deriving from different

theoretical traditions simultaneously.

Evidence in support of the construct validity of this set of measures to assess motivation

to learn is offered using a framework proposed by Messick (1989). Historically, construct

validity has been dealt with in different ways, with recent conceptualizations rejecting the

traditional three-part (construct, criterion, content) validity approachin favor of a more unified

validity theory (Messick, 1989; Pintrich, Wolters, & Baxter, 2000). In Messick’s (1989) unified

framework, construct validity is central and other forms of validity are subsumed under it.

Messick (1989) proposed a multidimensional framework for thinking about construct

validity, and described five kinds of evidence that can be used to support claims of construct

validity: content, substantive, structural, external, and generality of meaning. Content-related

evidence concerns how well the items reflect the content of the domain. Substantive evidence

concerns the relation between data and theory, and the guiding question is whether the data

generated by the instrument are consistent with the theory. Structural evidence, on the other

hand, concerns the relation between theory and the way the data are reduced: Do the scores

obtained reflect the complexities of the theoretical model? External evidence is perhaps the most

often considered, and questions include how the instrument relates to other measures of the same
 Validity and Motivation to Learn 4

construct, and whether the instrument relates to other constructs in theoretically sensible ways.

Finally, questions of generality of meaning revolve around how the findings generalize across

different populations, contexts, or subject-matter domains. In more recent work, Messick (1995)

specified an additional source of evidence, concerned with the intended and unintended

consequences of score use. This aspect of construct validity is key when tests are used for

assessment or placement decisions, as in the case of performance assessments or standardized

tests. Since a complete discussion of all of Messick’s sources of evidence is beyond the scope of

this paper, the focus has been narrowed to present substantive, structural, consequential and

generality of meaning evidence of the validity of a set of motivation-related measures.

The set of motivation-related measures included here draws from the most often-

researched theoretical frameworks in motivation literature today. These theoretical traditions are

characterized by different approaches to the study of the basic questions most research on

motivation in education tries to answer: What makes students want to learn in school? What

makes students feel competent? How do students’ wants and beliefs in the classroom influence

whether and how they approach learning? The research described here draws heavily on

expectancy-value theory, achievement goal theory, work on personal and situational interest, and

self-efficacy theory.

Self-efficacy refers to students’ beliefs that they have the resources and confidence to do

the tasks in the classroom (Bandura, 1986; Pintrich & Schunk, 2002). It is important that self-

efficacy be calibrated to one’s actual accomplishments (Pintrich & Schunk, 2002). As

intervention projects make changes and improve instruction, these reforms may require students

to think differently, to do math or science differently, and to engage the material in different

ways than is usual in mathematics and science classrooms. Besides beliefs about efficacy and
 Validity and Motivation to Learn 5

control, task value beliefs are another important motivational component (e.g., Eccles et al.,

1998; Pintrich & Schunk, 2002). Longitudinal research by Eccles and her colleagues (e.g.,

Eccles, et al., 1998; Fredericks, et al., 2002; Jacobs et al., 2002) has shown that student beliefs

about the importance and utility of mathematics leads them to enroll in more math courses in the

future. In addition, this research has shown that task value beliefs lead to enrollment or choices

to take more mathematics courses, but that once enrolled in the actual course, efficacy beliefs are

more strongly related to actual performance or achievement.

Personal interest refers to an individual's attraction to, or general liking and enjoyment of,

a specific activity or domain (Pintrich & Schunk, 2002). Eccles and her colleagues (Eccles, et al.,

1998) have shown that personal interest is an important component of motivation and functions

similarly to importance and utility value beliefs. In addition, other researchers have shown that

high levels of personal interest lead to more cognitive engagement, self-regulation, and

achievement (e.g., Koller, et al., 2001; Pintrich & Schunk, 2002). In many mathematics and

science reform projects, the goal is to increase student interest and positive attitudes towards

mathematics and science domains as well as interest in careers in these areas. It is an important

outcome in its own right, as well as a potentially important mediator of achievement (Koller et

al., 2001).

Another important component of student motivation concerns general achievement goals,

or students’ goals for academic learning in classroom contexts. The general distinction between

mastery and performance goals contrasts students who are mastery-oriented and focused on

learning and understanding and those students who are performance-oriented and focused on

doing better than others in terms of grades or other outcomes that invite interpersonal

comparisons (Pintrich, 2000a, b). Generally, mastery goals are positive and adaptive and lead to
 Validity and Motivation to Learn 6

more interest, engagement, and learning. Performance goals, on the other hand, can be adaptive

or maladaptive depending on whether students adopt an approach or avoid focus. Performance-

avoid goals where students are concerned about looking dumb or trying to avoid getting the

lowest scores are clearly maladaptive and are associated with less interest, engagement, and

lower levels of performance (Pintrich, 2000a, b). As intervention projects make changes and

improve instruction, it is important to understand how these different goals may motivate

students to learn and perform in different mathematics and science classrooms.

Method

Design

Data presented here were obtained through a collaboration between the

Motivation Assessment Program at the University of Michigan and a standards-based,

data-driven intervention program in the Southwest improve students’ academic

performance in mathematics. The collaboration included providing teachers with the

knowledge and tools to accurately diagnose students’ deficiencies, assess their progress,

adjust the curriculum and pedagogy, and transform the departmental culture to maximize

student learning in mathematics. Over the last two years, the partners have collaborated

to assess changes in motivation of the more than 14,000 students over the course of the

school year. Aggregated analyses of these data were disseminated to teachers and project

staff as part of professional development activities that serve as a major component of the

intervention, as well as through individual reports to teachers. The professional

development activities were designed to change administrative practices in the schools

and in the classrooms, effecting a cultural change that creates a sustainable climate of
 Validity and Motivation to Learn 7

improvement and achievement.

The partnership to date has involved five waves of student motivation surveys

over two school years, as well as two waves of teacher attitude and belief surveys. Data

from the beginning and end of the first year of student surveys are presented here.

Students were administered questionnaires by research assistants in their regular math

classrooms four weeks after the start of the school year and again approximately four

weeks before the end of the school year. All students in class on the day of administration

participated. Students were told that the purpose of the confidential survey was to elicit

their thoughts and feelings about the subject of math and their own math class. Students

were guided through a sample item and then completed a 110- question survey during

their math period. Items were read aloud to the middle school students; high school

students worked through the survey independently after receiving instructions from

trained research assistants. The survey took approximately 30 min. to complete. The

teacher was present in the room while the survey was being completed, but remained

seated and unobtrusive, unable to view any of the survey responses.

Participants

Analyses presented here are based on 8,429 students (49% female) from 487

classrooms in 14 ethnically diverse, working class public middle and high schools in

California (72% Latino/a, 16% Vietnamese, 6% Caucasian, 6% Other - primarily SE

Asian). Between 60 and 75% of the students in these schools were eligible to receive free

or reduced lunch. Two of the four districts have been characterized as high-need districts

by the state and there is a sizable population of English Learners.

 Validity and Motivation to Learn 8

Measures

The student motivation survey included measures of self-efficacy for learning

math and solving math problems, task value for, and students’ personal achievement

goals. Items were rated on a 5-point Likert scale (1 = not at all true; 3 = somewhat true; 5

= very true), and all questions were worded to have students focus on the domain of

mathematics.

Task Value was measured with 18 items, which included four components

adapted from previous work. Interest (6 items, ! = .95) referred to students’ attraction to,

liking for, and enjoyment of math. (e.g., “I find math very interesting”). Utility (6 items,

! = .87) was concerned with students’ beliefs about the usefulness of math as an area of

study (e.g., “Math is useful to me for things I do outside of school”). While utility value

focused on the importance of math as a means to an end, attainment value focused on the

value of math as part of a student’s identity. Attainment value (6 items, ! = .87) referred

to students’ judgments about the importance of math for their sense of who they are (e.g.,

“Thinking mathematically is an important part of who I am”). Cost value (2 items, ! =

.81) tapped students’ judgments about the amount of effort required to be successful in

math (e.g., “Success in math requires that I give up other activities I enjoy”).

Efficacy (8 items, ! = .88) items assessed students’ judgments about their ability

and confidence to perform adequately in math (e.g., “How sure are you that you can do

even the most difficult math work”). Achievement goals (three 5-item scales items, !s =

.87, .84, .79) referred to students’ purposes when approaching, engaging in, and

responding to math instruction. Mastery goals focused on learning and understanding

(e.g., “My goal in math is to learn as much as I can”), performance-approach goals

 Validity and Motivation to Learn 9

focused on demonstrating ability and outperforming others (e.g., “My goal in math is to

look smarter than other students”), and performance-avoid goals focused on not looking

dumb (e.g., “My goal in math is to avoid looking like I can’t do my work”).

Substantive Evidence

Substantive evidence is concerned with the internal relations among the items in

an instrument. The guiding question is whether the data generated by the instrument are

consistent with the theory of the construct. The measures of task value and achievement

goals included in our assessment would show substantive evidence of construct validity,

according to Messick (1989), if the number and type of scores generated was consistent

with the theories from which the items were developed. In the case of task value, four

components are predicted: interest, utility, attainment, and cost. Previous studies have

sometimes had difficulty finding the predicted distinctions between utility value and

attainment value. Results from exploratory factor analyses of the task value items are

presented in tables 1 (beginning of school year) and 2 (end of school year).

Structural Evidence

This component of construct validity asks whether the scoring of the instrument

reflects the complexities of the theory. A single total score indicates a unitary construct,

while a combination of composite scores and subscores indicates a hierarchical construct.

Subsumed under the structural component are issues related to scale reliability. With

older validity theories, reliability was separate from validity. This made it possible to

have scales that were valid, but not reliable, or scales that were reliable, but not valid.

With Messick’s (1989) unified approach to construct validity, issues of reliability are

factored into judgments of validity. Reliabilities for the achievement goal scales for this
 Validity and Motivation to Learn 10

sample are in line with previous work, which usually shows high reliability as assessed

by indices of internal consistency (! " .85) for the mastery and performance approach

scales, but lower reliability for the avoidance scales. Table 3 shows reliabilities for both

waves, presented separately for the middle and high school students.

Consequential Evidence

A critical component of construct validity for our project concerns the intended

and unintended consequences of score use. Messick’s discussions dealt with performance

assessments or standardized tests and the use of those scores for assessment or placement

decisions. Motivation-related data are not typically associated with these same kinds of

immediate consequences for students, however the nature of our partnership is not

typical. Much of our work is focused on disseminating data to teachers through

professional development that targets change in teacher practice to support adaptive

motivation for students. Figure 1 presents a sample report generated for a participating

school. Such reports are used in professional development workshops to help teachers

and schools find areas of strength and weakness from a motivational perspective. The

decision to present these results in their full complexity was made in response to a

general tendency to oversimplify motivation in the classroom. For example, teachers

would report that their students were simply “unmotivated.” Therefore, one aim of our

project was to show that there were different ways for students to be motivated (and

unmotivated), and that these different ways required different interventions from teachers

when problems arose.

Results were presented to teachers separated by course, because teachers wanted

to address motivational concerns in course-alike teams. The quality of the motivational

 Validity and Motivation to Learn 11

problems with Algebra 1 students was quite different from the quality of the problems

with Pre Calculus students. In response, we structured the reporting of data to support

these conversations.

The intended consequences of the use of these scores included changes in the

content of professional development and changes in the schools’ action plans. Along with

the graphs, we provided interpretations designed to facilitate discussion. This is an

excerpt describing Algebra 1A students in middle school “A”:

• Algebra 1A students started the year with the lowest scores on interest, mastery,
and efficacy (e.g., they saw math as less interesting than other math students, they
were less likely to focus on understanding, and were the least confident in their
math ability). However, they saw math as just as useful as other students in the
school, and had similar levels of focus on competition.
o Change –Algebra 1A students had a more adaptive pattern of change than
other students at this school; the drops were generally smaller than for
students in other courses. They saw math as less useful and were less
focused on learning but slightly more confident in their math ability.
o Goals for next year – Help students see how math is useful, and more
importantly, use TARGET TIpS to help focus students on learning and
developing (rather than just demonstrating) ability.

With 14 different schools as collaborators, we have seen variation in the degree to

which the motivational data have had consequences in terms of teacher practices, and

student outcomes. In some schools, detailed yearly action plans have been revised to

include a focus on motivation. In one school the drop in student interest presented the

biggest concern for teachers, and the math department has made supporting student

interest a major focus. It is more difficult to report at this point on the unintended

consequences of score use, but such questions will be important to examine as we

continue to examine the construct validity of our set of motivation measures.

 Validity and Motivation to Learn 12

Evidence of Generality of Meaning

This component of construct validity concerns how the scores on an instrument

generalize to other populations and contexts. Of particular interest for this set of

motivation measures are characteristics of the sample. This study included an ethnically

diverse sample of 6th through 12th students, and we found the decline in motivation across

middle and high school reported in other studies (e.g., Anderman, Maehr, & Midgley,

1999). Figures 2 and 3 show beginning and end of school year motivation profiles,

separated for each grade level.

Students showed expected drops in motivation over the course of the school year.

They became less interested, saw math as less useful, and felt less confident in their

ability to understand math. In addition, they reported lower levels of achievement goals,

with lower means on all three goals. While a decreased focus on mastery goals of

learning and understanding is problematic, the associated decrease in a focus on

competition should be considered an adaptive change.

The overall decrease in motivation across the school year played a smaller role in

professional development than the variability we found across schools and between

courses. Looking at variability across courses formed the basis for much of the dialogue

during professional development activities. For example, sixth graders were particularly

disadvantaged over the school year, with the greatest drop-offs over the year. They were

less interested, considered math less useful, and were less confident in their math

abilities. A positive change was the decreased focus on competition and not looking

incompetent, but this was accompanied as well by less of a focus on learning and

understanding. Targeted professional development with sixth grade teachers has focused
 Validity and Motivation to Learn 13

on supporting mastery goals, interest, and value over the school year. Other questions of

generality of meaning concern possible gender or ethnic differences, or even domain-

specific differences, in the structure of these constructs. As data from different

populations and science intervention projects becomes available, more evidence of the

generality of these measures will be investigated.

Discussion

Exploratory factor analysis, reliability analysis, and figures showing grade-level

differences are offered here as substantive, structural, and generality of meaning evidence

in support of the construct validity of this set of measures of motivation to learn. Further,

a discussion of the ways in which these data have been reported and used serves as

consequential evidence. Though these sources of evidence have been separated for clarity

of discussion, it is important to remember that there exists considerable overlap, and that

these aspects of construct validity are part of a unified validity theory that does not rely

on nor require any one form of evidence.

 Validity and Motivation to Learn 14

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Educational Psychology, 84, 261-271.

Anderman, E. M., M. L. Maehr, et al. (1999). Declining motivation after the transition to

middle school: Schools can make a difference. Journal of Research and Development

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Eccles, J. S. and C. Midgley (1989). Stage-environment fit: Developmentally appropriate

classrooms for young adolescents. In C. Ames and R. Ames (Eds.), Research on

motivation in education (pp. 139-186). San Diego, CA: Academic Press.

Eccles, J., Wigfield, A., Harold, R.D., & Blumenfeld, P. (1993). Age and gender

differences in children’s self- and task perceptions during elementary school. Child

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Eccles, J., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In W. Damon

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outcomes of math and science instruction. Paper presented as part of a symposium entitled:
 Validity and Motivation to Learn 15

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 Validity and Motivation to Learn 17

Table 1

Factor Loadings for Task Value Measures at Beginning of School Year (N = 8,429)

1 2 3 4
1. I enjoy doing math. 0.95
2. I like math. 0.93
3. I enjoy the subject of math. 0.88
4. How much do you like doing math? 0.85
5. Math is exciting to me. 0.83
6. I am fascinated by math. 0.75
7. Math will be useful for me later in
life. 0.94
8. Math concepts are valuable because
they will help me in the future. 0.83
9. How useful is learning math for what
you want to do after you graduate and
go to work? 0.71
10. In general, how useful is what you
learn in math? 0.60
11. Being good at math will be important
when I get a job or go to college. 0.52
12. Compared to most of your other
school subjects, how useful is what
you learn in math? 0.46
13. I have to give up a lot to do well in
math. 0.79
14. Success in math requires that I give
up other activities I enjoy. 0.77
15. It is important for me to be someone
who is good at solving problems that
involve math. 0.79
16. Being someone who is good at math
is important to me. 0.79
17. Being good at math is an important
part of who I am. 0.74
18. It is important to me to be a person
who reasons mathematically. 0.63
19. I feel that, to me, being good at
solving problems which involve math
or reasoning mathematically is 0.61
20. Thinking mathematically is an
important part of who I am. 0.55
Note: Factor loadings under .20 have been omitted.
 Validity and Motivation to Learn 18

Table 2

Factor Loadings for Task Value Measures at End of School Year (N = 8,429)

1 2 3 4
1. I enjoy doing math. 0.96
2. I like math. 0.94
3. I enjoy the subject of math. 0.86
4. How much do you like doing math? 0.86
5. Math is exciting to me. 0.83
6. I am fascinated by math. 0.76
7. Math will be useful for me later in life. 0.96
8. Math concepts are valuable because
they will help me in the future. 0.88
9. How useful is learning math for what
you want to do after you graduate and
go to work? 0.75
10. In general, how useful is what you
learn in math? 0.65
11. Being good at math will be
important when I get a job or go to
college. 0.61
12. Compared to most of your other
school subjects, how useful is what
you learn in math? 0.51
13. I have to give up a lot to do well in
math. 0.83
14. Success in math requires that I give
up other activities I enjoy. 0.82
15. It is important for me to be someone
who is good at solving problems that
involve math. 0.81
16. Being someone who is good at math
is important to me. 0.80
17. Being good at math is an important
part of who I am. 0.80
18. It is important to me to be a person
who reasons mathematically. 0.73
19. I feel that, to me, being good at
solving problems which involve
math or reasoning mathematically is 0.65
20. Thinking mathematically is an
important part of who I am. 0.63
Note: Factor loadings under .20 have been omitted.
 Validity and Motivation to Learn 19

Table 3

Reliabilities for Efficacy, Task Value, and Achievement Goal Measures for Beginning of

School Year (N = 8,429)

Middle School High School Combined

Interest Value
0.95 0.96 0.96
Utility Value
0.87 0.84 0.87
Attainment Value
0.87 0.86 0.87
Cost Value
0.79 0.71 0.75

Personal Mastery Approach Goals

0.86 0.87 0.87
Personal Performance Approach Goals
0.84 0.84 0.84
Personal Performance Avoid Goals
0.80 0.78 0.79

Efficacy
0.89 0.87 0.88
 Validity and Motivation to Learn 20

Table 4

Reliabilities for Efficacy, Task Value, and Achievement Goal Measures for End of School

Year (N = 8,429)

Middle School High School Combined

Interest Value
0.95 0.96 0.95
Utility Value
0.90 0.88 0.89
Attainment Value
0.90 0.88 0.89
Cost Value
0.83 0.78 0.81

Personal Mastery Approach Goals

0.88 0.88 0.88
Personal Performance Approach Goals
0.86 0.86 0.86
Personal Performance Avoid Goals
0.84 0.82 0.83

Efficacy
0.91 0.91 0.91
 Validity and Motivation to Learn 21

Figure 1

Sample School Report

 Validity and Motivation to Learn 22

Figure 2

Cross-sections of Beginning-of-Year Motivation Profiles for Middle and High School

Students (N = 8,429)
 Validity and Motivation to Learn 23

Figure 3

Cross-sections of End-of-Year Motivation Profiles for Middle and High School Students

(N = 8,429)

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